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Case Study Egoditor

How Zeile7 created a lean & cloud-based data delivery & analytics pipeline for Egoditor

Egoditor (now Europe) was looking for a quicker and more efficient way to extract and leverage actionable insights from its data.
They chose Zeile7 — an expert in end2end digitalisation and software engineering — to build a lean & nimble data delivery and analytics platform from scratch. Egoditor needed to expose its data in such a way that analytics could happen as close to real time as possible. Together, Egoditor and Zeile7 created a cloud-based data solution that delivered clean data to all parts of the company and enabled quick and targeted decision-making.

Key Challenges

Egoditor continually sources QR code data – where and how the codes are used, how visuals and industry preferences affect usage, etc. – but the data was not channeled to the different teams inside the company and hence wasn’t generating rich insights. Those teams required a transparent and reliable data pipeline that could deliver immediate, robust data with responsive analytics to help them make critical business decisions—for example, where to shift marketing budget, how to prioritise the tech backlog, and how to predict financial performance.
The new pipeline required integrating diverse data sources, some direct integrations (for example, with the PSP) and a flexible platform, which would adapt to higher volumes of data as need be.



Zeile7 worked with Egoditor to reimagine its data pipeline. Z7 conducted thorough analysis of the needs and requirements, existing and desired capabilities, and put together a mixed team of external and internal resources who would push the data at Egoditor to the next level.

Continuing in the chosen technology by the company – Google Cloud Platform – Z7 used the data warehousing capabilities of Google Big Query exposing the data into Tableau. The raw data was cleaned and each pipeline layer increased the data quality and reliability. Z7 strengthened the team and introduced methodologies for iterative and rapid data delivery. The broader management team gradually realised their role as data champions and engaged actively with the data value stream.



  • Created the data value stream from scratch.
  • Transformed a complex and difficult-to-maintain tool environment, where each tool had a different interpretation of the same metric, into a single-source of truth setup with configurable parameters.
  • A stable serverless pipeline that extracts, transforms, and loads data from several sources (AWS application database with handover after Kinesis streams and an API/PubSub, payment service provider direct integration over API, etc.), exposing only verified and clean data to the data mart layer.
  • Scalable solution using Google Cloud Platform: analytical data warehouse with compliant data management and layered access to data.
  • A clean layer of data (data mart) with information points useful for several departments, enabling reliable self-service reporting along the centrally created dashboards around business health KPIs.
  • A suite of SaaS KPIs adapted to the business model of Egoditor.
  • Provided a light-weight, flexible, easy to understand and operate data setup, able to grow from descriptive (reports and dashboards), to predictive (forecasting), to prescriptive data (machine learning).
  • Cold and warm data storage (a compliant backup strategy).
  • Created a solid team from internal and external resources. Upskilled available talent and installed healthy work practices.

Technologies used

About the author

Denitsa Schmidt

ex Zeile7 Coming with a PhD in International business and a law degree, she is a certified Salesforce admin and Scrum PO. Denitsa is expert for growth, expension, revenue as well as CRM & ERP systems

ex Zeile7 Coming with a PhD in International business and a law degree, she is a certified Salesforce admin and Scrum PO. Denitsa is expert for growth, expension, revenue as well as CRM & ERP systems

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